1. Field of the Invention
The present invention relates to a mobile communication system, and more particularly to an apparatus and a method for estimating the velocity of a mobile station in Rician fading environments in which a direct wave exists.
2. Description of the Related Art
In the next generation wireless communication, a system resource allocation based on exact channel information plays an important role in supporting a large quantity of multimedia packet service in restricted frequencies and channel resources.
In a time-varying channel such as a mobile communication channel, velocity information is very important information for a channel. The velocity information is information representing channel states of a user, which is an inevitable necessity for efficiently managing system resources.
Conventionally, many adaptive algorithms use velocity information of a mobile station in determining the coefficient (e.g., channel tracker length and interleaver size) of an adaptive receiver in mobile communication environments. In particular, velocity information is very important information in processing power control signals for controlling power in order to solve a near-far problem and handoffs occurring when a mobile station passes through a boundary between cells.
A mobile station experiences a Doppler shift according to its own movement. The Doppler shift generates a frequency error of a received signal in proportion to a movement velocity of the mobile station for a base station.
Accordingly, it is possible to estimate the velocity of the mobile station using a characteristic in which the frequency error of the received signal due to the Doppler shift is proportional to the movement velocity of the mobile station as described above. That is, the velocity of the mobile station may be estimated by detecting a maximum Doppler frequency of the received signal in a mobile communication system.
Estimation of a maximum Doppler frequency has a large role in estimating a channel coefficient. Various algorithms used for estimating the maximum Doppler frequency are presently known.
A conventional method for estimating the maximum Doppler frequency includes a method for inducing a Level Crossing Rate (LCR) characteristic and a Zero Crossing Rate (ZCR) characteristic of a random signal, a method using an autocorrelation function (ACF) value of a received signal, and a method using covariance (COV) of a square value for a magnitude of a received signal.
The conventional technology may obtain a precisely estimated velocity value in Rayleigh fading environments in which direct waves do not exist. However, the conventional technology may exactly estimate the velocity of a mobile station only by using a Rician coefficient K, which represents a power ratio of a direct wave component and a scattered wave component, and information for an incident angle θ0 of a direct wave in Rician fading environments in which the direct waves exist.
A method for estimating the Rician coefficient K is already well known. However, a method for estimating the information for the incident angle θ0 of the direct wave is not yet known in a system using a single antenna.
Accordingly, it is difficult to apply the conventional technology to the Rician fading environments in which the direct waves exist such as mobile communication environments.
Generally, when the conventional technology is applied without considering the direct wave, a method using an LCR value or a COV value is tolerant to the Rician fading environments in which the direct waves exist, as compared with a method using a ZCR value or an ACF value. However, because the method using the LCR value or the COV value does not consider the direct wave, very serious problem may occur in that error due to the direct waves reaches 20% to 40% of an estimated value.
In order to compensate for this disadvantage, a method has been proposed, which uses a coefficient of an Auto-Regressive (AR) model of a fading channel. The method using the coefficient of the AR model shows little error in the Rician fading environments, but it still has an error of about 20% with respect to a specific incident angle. Further, a process of estimating the coefficient of the AR model is sensitive to additive noise.